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Aerospace
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Artificial Intelligence in Aerospace Propulsion
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Artificial Intelligence in Aerospace Propulsion

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 7215

Special Issue Editor

Prof. Dr. Kan Xie

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: aerospace propulsion technology; multiple-field-coupling fluid mechanics and jet control; propellant mechanical behavior in complicated conditions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerospace propulsion is a key technology for space missions and exploration. Traditionally, the study of aerospace propulsion is based on experiments and numerical calculations; however, with the development of artificial intelligence (AI), AI technologies including machine learning, data-driven technologies, and so forth, have come to be applied in research on aerospace propulsion. This Special Issue in Aerospace, entitled "Artificial Intelligence in Aerospace Propulsion", features a collection of articles exploring the latest advancements in artificial intelligence technologies for aerospace propulsion. It covers a variety of topics, including AI technologies in applications such as advanced rocket engines, hybrid propulsion systems, electric thrusters, and plasma-based propulsion. The papers will discuss various aspects of methodology, modeling, design, analysis, and testing for propulsion systems, highlighting the challenges and opportunities associated with AI technologies in aerospace propulsion.

The insights presented in this Special Issue will provide valuable information for researchers, professionals, and students involved in aerospace engineering and related fields. Overall, this Special Issue provides a comprehensive survey of the recent trends, innovations, and the future prospects of artificial intelligent technologies for aerospace propulsion.

Prof. Dr. Kan Xie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • data-driven
  • digital twin
  • chemical propulsion
  • electric propulsion
  • rocket engine
  • plasma propulsion

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Published Papers (5 papers)

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25 pages, 2874 KB  
Article
Temporal-Enhanced GAN-Based Few-Shot Fault Data Augmentation and Intelligent Diagnosis for Liquid Rocket Engines
by Hui Hu, Rongheng Zhao, Chaoyue Xu, Shuai Ren and Hui Wang
Aerospace 2026, 13(4), 306; https://doi.org/10.3390/aerospace13040306 - 25 Mar 2026
Viewed by 338
Abstract
(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework [...] Read more.
(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aerospace Propulsion)
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35 pages, 10077 KB  
Article
Physically Interpretable and AI-Powered Applied-Field Thrust Modelling for Magnetoplasmadynamic Space Thrusters Using Symbolic Regression: Towards More Explainable Predictions
by Miguel Rosa-Morales, Matthew Ravichandran, Wenjuan Song and Mohammad Yazdani-Asrami
Aerospace 2026, 13(3), 245; https://doi.org/10.3390/aerospace13030245 - 5 Mar 2026
Viewed by 456
Abstract
Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure [...] Read more.
Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aerospace Propulsion)
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16 pages, 2724 KB  
Article
The Modulatory Effect of Inhibitors on the Thermal Decomposition Performance of Graded Al@AP Composites
by Kan Xie, Jing Wang, Zhi-Yu Zhang, Bin Tian, Su-Lan Yang, Jingyu Lei and Ming-Hui Yu
Aerospace 2025, 12(4), 298; https://doi.org/10.3390/aerospace12040298 - 31 Mar 2025
Viewed by 1089
Abstract
In this paper, a series of graded Al-based composites, including Al@AP, Al@AP/BM−52, and Al@AP/BPE−1735, have been prepared by spray drying technology. The thermal decomposition characteristics, kinetic parameters of the decomposition reaction, and Pyro-GC/MS products were comprehensively investigated. The results showed that two inhibitors, [...] Read more.
In this paper, a series of graded Al-based composites, including Al@AP, Al@AP/BM−52, and Al@AP/BPE−1735, have been prepared by spray drying technology. The thermal decomposition characteristics, kinetic parameters of the decomposition reaction, and Pyro-GC/MS products were comprehensively investigated. The results showed that two inhibitors, BM−52 and BPE−1735, had a significant effect on the thermal decomposition of AP. The addition of BM−52 conspicuously enhanced the thermal interaction, resulting in a more complete decomposition reaction of AP. Meanwhile, the incorporation of BPE−1735 significantly enhanced the heat releases of AP, leading to a significant enhancement in the energetic performance during the decomposition process of AP. BM−52 and BPE1735 inhibit AP decomposition as evidenced by higher activation energies for thermal decomposition and altered physical models of decomposition. Pyro-GC/MS results reveal that the fundamental pathway of Al@AP thermal decomposition remains unaltered by BM−52. However, the proportion of oxygen-containing compound products is moderately reduced. In contrast, for Al@AP/BPE−1735, in addition to the same products as those from Al@AP pyrolysis, new pyrolysis peaks emerge. It is implied that specific chemical reactions or interactions are triggered during the thermal decomposition process, thereby resulting in the formation of distinct chemical species. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aerospace Propulsion)
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15 pages, 1694 KB  
Article
SSMBERT: A Space Science Mission Requirement Classification Method Based on BERT
by Yiming Zhu, Yuzhu Zhang, Xiaodong Peng, Changbin Xue, Bin Chen and Yu Cao
Aerospace 2024, 11(12), 1031; https://doi.org/10.3390/aerospace11121031 - 17 Dec 2024
Viewed by 1419
Abstract
Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on [...] Read more.
Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aerospace Propulsion)
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20 pages, 9508 KB  
Article
A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency
by Jinwoo Song, Seong Hee Cho, Seokgoo Kim, Jongwhoa Na and Joo-Ho Choi
Aerospace 2024, 11(9), 741; https://doi.org/10.3390/aerospace11090741 - 10 Sep 2024
Cited by 4 | Viewed by 1805
Abstract
In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic [...] Read more.
In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aerospace Propulsion)
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